A Calibrated and Automated Simulator for Innovations in 5G

The rise of 5G deployments has created the environment for many emerging technologies to flourish. Self-driving vehicles, Augmented and Virtual Reality, and remote operations are examples of applications that leverage 5G networks' support for extremely low latency, high bandwidth, and increased throughput. However, the complex architecture of 5G hinders innovation due to the lack of accessibility to testbeds or realistic simulators with adequate 5G functionalities. Also, configuring and managing simulators are complex and time consuming. Finally, the lack of adequate representative data hinders the data-driven designs in 5G campaigns. Thus, we calibrated a system-level open-source simulator, Simu5G, following 3GPP guidelines to enable faster innovation in the 5G domain. Furthermore, we developed an API for automatic simulator configuration without knowing the underlying architectural details. Finally, we demonstrate the usage of the calibrated and automated simulator by developing an ML-based anomaly detection in a 5G Radio Access Network (RAN).

Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes

Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models. State-of-art approaches for semi-supervised learning rely on student-teacher models trained using a multi-stage process, and considerable data augmentation. Custom networks have been developed for the weakly-supervised setting, making it difficult to adapt to different detectors. In this paper, a weakly semi-supervised training method is introduced that reduces these training challenges, yet achieves state-of-the-art performance by leveraging only a small fraction of fully-labeled images with information in weakly-labeled images. In particular, our generic sampling-based learning strategy produces pseudo-ground-truth (GT) bounding box annotations in an online fashion, eliminating the need for multi-stage training, and student-teacher network configurations. These pseudo GT boxes are sampled from weakly-labeled images based on the categorical score of object proposals accumulated via a score propagation process. Empirical results on the Pascal VOC dataset, indicate that the proposed approach improves performance by 5.0% when using VOC 2007 as fully-labeled, and VOC 2012 as weak-labeled data. Also, with 5-10% fully annotated images, we observed an improvement of more than 10% in mAP, showing that a modest investment in image-level annotation, can substantially improve detection performance.

Structure-aware reinforcement learning for node-overload protection in mobile edge computing

Mobile Edge Computing (MEC) refers to the concept of placing computational capability and applications at the edge of the network, providing benefits such as reduced latency in handling client requests, reduced network congestion, and improved performance of applications. The performance and reliability of MEC are degraded significantly when one or several edge servers in the cluster are overloaded. Especially when a server crashes due to the overload, it causes service failures in MEC. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings - computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic) but requires an order of magnitude less time to train, outputs easily interpretable policy, and can be deployed in an online manner.